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Tracking of Cells in a Sequence of Images Using a Low-Dimension Image Representation

Abstract : We propose a new image analysis method to segment and track cells in a growing colony. By using an intermediate low-dimension image representation yielded by a reliable over-segmentation process, we combine the advantages of two-steps methods (possibility to check intermediate results) and the power of simultaneous segmentation and tracking algorithms, which are able to use temporal redundancy to resolve segmentation ambiguities. We improve and measure the tracking performances with a notion of decision risk derived from cell motion priors. Our algorithm permits to extract the complete lineage of a growing colony during up to seven generations without requiring user interaction.
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Contributor : Lionel Moisan <>
Submitted on : Monday, December 17, 2007 - 7:38:49 PM
Last modification on : Tuesday, December 8, 2020 - 9:50:37 AM
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Maël Primet, Alice Demarez, François Taddei, Ariel Lindner, Lionel Moisan. Tracking of Cells in a Sequence of Images Using a Low-Dimension Image Representation. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, Paris, France. pp.995 - 998, ⟨10.1109/ISBI.2008.4541166⟩. ⟨hal-00198779⟩



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